I am currently a Research Associate at the Computer Science Department of UCL. My primary research focus is the analysis of human-generated content published on the Web, mainly on Social Media platforms. I am also interested in interdisciplinary research tasks that bring together Computer Science, Healthcare, Statistics and Social Sciences.

News snippets

We gave a tutorial at WSDM '15 on “Learning about health and medicine from Internet data” [ slides ]

What are the most important factors for determining user impact on Social Media platforms? Can we identify user actions that have a significant effect on their impact? In this work, we propose a set of nonlinear models based on Gaussian Processes for inferring user impact on Twitter. Our modelling is based on actions under the direct control of a user, including textual features such as word or topic (word-cluster) frequencies. Given the strong inference performance, we then dig further into our models and qualitatively analyse their properties from a variety of angles in an effort to discover the specific user behaviours that are decisive impact-wise. A brief summary of this work is given in this blog post.

Affective patterns in books

What happens if we quantify affective expression in millions of books? We can probably identify periods with dominant emotions, extract temporal emotion patterns through the century and come up with interesting scenarios that may explain them (PLOS ONE, 2013). Additionally, we could explain those patterns by looking at their reflection in real-world tendencies such as indices about the main driving factor of the system we are living in, the economy (PLOS ONE, 2014).

An effort to assess the statistical robustness of the above findings together with comparative figures across different emotion detection tools are presented in this paper (Big Data '13).

Can we exploit text generated by Social Media (e.g. Twitter) users to quantify the magnitude of events, such as an infectious disease (e.g. flu) or even a rainfall by applying Machine Learning methods?

This is the first work showing that Social Media can be used to track the level of an infectious disease, such as influnza-like-illness (ILI), in the population. To achieve that we collected geolocated tweets from 54 UK cities, used them in a regularised regression model which was trained and evaluated against ILI rates from the Health Protection Agency. Flu Detector is a demonstration that used (now stopped!) the content of Twitter for nowcasting the level of flu-like illness in several UK regions on a daily basis. We've recently came up with an improved visualisation of predicted flu rates from Twitter data, but it is still in its alpha version.

Mood of the Nation used (now stopped!) more than half a million geolocated tweets on a daily basis to detect mood and affect trends in the UK population focus on 4 categories of affect: joy, sadness, anger and fear. A simple assessment of those patterns reveals quite interesting results. Check this out for example!